Cost-effective Monitoring System Research Articles

Bi-Level Interturn Short-Circuit Fault Monitoring for Wind Turbine Generators With Benchmark Dataset Development

Abstract Flourished wind energy market pushes the latest wind turbines (WTs) to further and harsher inland and offshore environment. Increased operation and maintenance cost calls for more reliable and cost effective condition monitoring systems. In this article, a bi-level condition monitoring framework for interturn short-circuit faults (ITSCFs) in WT generators is proposed. A benchmark dataset, consisting of 75 ITSCF scenarios and generator current signals of a specific WT, has been created and made publicly available on Zenodo. The data are simulated at a rate of 4 kHz. Based on the time and frequency features extracted from data processing, machine learning-based severity estimation and faulty phase identification modules can provide valuable diagnostic information for wind farm operators. Specifically, the performance of long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs) are analyzed and compared for severity estimation and faulty phase identification. For test-bed experimental reference, various numbers of scenarios for training the models are analyzed. Numerical experiments demonstrate the computational efficiency and robust denoising capability of the CNN algorithm. The GRU network, however, achieves the highest accuracy. The overall system performance improves significantly, from 87.76% with 16 training scenarios to 99.95% with 52 training scenarios, when tested on a set containing all 76 scenarios from an unforeseen period.

Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks

Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is increasing interest in proactively diagnosing bearing faults by analyzing signals obtained from bearings. Although numerous studies have introduced machine learning methods for bearing fault diagnosis, the high costs associated with sensors and data acquisition devices limit their practical application in industrial environments. Additionally, research aimed at identifying the root causes of faults through diagnostic algorithms has progressed relatively slowly. This study proposes a cost-effective monitoring system to improve economic feasibility. Its primary benefits include significant cost savings compared to traditional high-priced equipment, along with versatility and ease of installation, enabling straightforward attachment and removal. The system collects data by measuring the vibrations of both normal and faulty bearings under various operating conditions on a test bed. Using these data, a deep neural network is trained to enable real-time feature extraction and classification of bearing conditions. Furthermore, an explainable AI technique is applied to extract key feature values identified by the fault classification algorithm, providing a method to support the analysis of fault causes.

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

Landslide Monitoring and Warning System Based on Wireless Sensor Network

Landslides are common in Indonesia, often caused by factors like heavy rainfall and high soil moisture levels. The Batubrak mini hydro power plant intake area in Lampung province of Indonesia has experienced landslides, prompting the need for an early warning system. This study presents the development of a cost-effective and efficient real-time landslide monitoring and warning system. The system incorporates sensors to measure accelerometer and vibration parameters, enabling real-time data display through a wireless sensor network and SCADA software. Early warnings are issued when triggers are activated. Three sensor nodes were deployed in the intake area for testing, demonstrating their ability to detect, collect, process, and transmit accelerometer and vibration data to the gateway node.

Nanostructured NiS2-based flexible smart sensors for human respiration monitoring.

The growing demand for wearable healthcare devices has led to an urgent need for cost-effective, wireless and portable breath monitoring systems. However, it is essential to explore novel nanomaterials that combine state-of-the-art flexible sensors with high performance and sensing capabilities along with scalability and industrially acceptable processing. In this study, we demonstrate a highly efficient NiS2-based flexible capacitive sensor fabricated via a solution-processible route using a novel single-source precursor [Ni{S2P(OPr)2}2]. The developed sensor could precisely detect the human respiration rate and exhibit rapid responsiveness, exceptional sensitivity and selectivity at ambient temperatures, with an ultra-fast response and recovery. The device effectively differentiates the exhaled breath patterns including slow, fast, oral and nasal breath, as well as post-exercise breath rates. Moreover, the sensor shows outstanding bending stability, repeatability, reliable and robust sensing performance and is capable of contactless sensing. The sensor was further employed with a user-friendly wireless interface to facilitate smartphone-enabled real-time breath monitoring systems. This work opens up numerous avenues for cost-effective, sustainable and versatile sensors with potential applications for Internet of Things-based flexible and wearable electronics.This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations

A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations.

The potential of a novel enzyme-based surface plasmon resonance biosensor for direct detection of dopamine

Dopamine is one of the significant neurotransmitters and its monitoring in biological fluids is a critical issue in healthcare and modern biomedical technology. Here, we have developed a dopamine biosensor based on surface plasmon resonance (SPR). For this purpose, the carboxymethyl dextran SPR chip was used as a surface to immobilize laccase as a bioaffinity recognition element. Data analysis exhibited that the acidic pH value is the optimal condition for dopamine interaction. Calculated kinetic affinity (KD) (48,545 nM), obtained from a molecular docking study, showed strong association of dopamine with the active site of laccase. The biosensor exhibited a linearity from 0.01 to 189 μg/ml and a lower detection limit of 0.1 ng/ml (signal-to-noise ratio (S/N) = 3) that is significantly higher than the most direct dopamine detecting sensors reported so far. Experiments for specificity in the presence of compounds that can co-exist with dopamine detection such as ascorbic acid, urea and l-dopa showed no significant interference. The current dopamine biosensor with high sensitivity and specificity, represent a novel detection tool that offers a label-free, simple procedure and cost effective monitoring system.

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification.

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 μW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

In-situ monitoring of sintering and analytical modeling of densification and shrinkage in binder jetted 316 L stainless steel

In binder jetting, shrinkage and deformation occur during the sintering step, both of which are affected by the green density of the binder jetted materials. The study innovatively introduces a cost-effective, practical, and in-process monitoring system for visualizing shrinkage and deformation on larger samples than conventionally observed using small-scale specimens in dillatometry equipment. The powder characteristics and binder jet printing process itself influence the initial green density. The comprehensive analysis of powder flowability and packing density, densification behavior, and shrinkage reveals that the consolidated parts using virgin powder (with a green density of 55 %) can achieve a relative density above 99.9 % with an anisotropic shrinkage in the Z > X > Y direction. In contrast, the used or recycled powder exhibits a lower green density of ∼48 %, higher shrinkage rate in all three dimensions, and a decreased degree of anisotropy. Using in-process imaging and experimental data on the grain size attained through optical microscopy and electron backscatered diffraction imaging, the material's shear and bulk viscosities were determined. The formation of delta-ferrite and its impact on densification were discussed in the context of solid-state and supersolidus liquid phase sintering. The model relied on the continuum sintering theory formulated by Skorohod and Olevsky. The strain evolution from the in-situ imaging of sintering process is correlated with porosity based on the used feedstock and applied sintering temperatures. The outcomes of this study offer valuable perspectives on anisotropic sintering mechanisms, bridging the knowledge gap regarding the relationships between structures produced through binder jetting and subsequent sintering of materials.

Remotely controlled smart monitoring system of hermetic paddy storage to reduce postharvest losses in Bangladesh

In Bangladesh, where agriculture forms the backbone of the economy, efficient grain storage is crucial, particularly for paddy storage. Traditional methods like gunny bags and plastic containers are common, but the recent introduction of hermetic storage technologies, such as GrainPro bags and hermetic cocoons, have significantly reduced postharvest losses paddy. However, most research focuses on oxygen (O2) and carbon dioxide (CO2) levels in those open storage systems. This study introduces an innovative, cost-effective remote monitoring system for hermetically sealed grain storage, designed using locally sourced components, including a Digital Hygrometer, WiFi Mini IP Camera, and a Remote Controller unit. Tested over a 125-day period at Advanced Storage Lab, Bangladesh Agricultural University, the system effectively monitored the internal environment of the storage units. Key findings show that the internal temperature of the storage bags varied between 16.7°C and 29.2°C, demonstrating lesser fluctuation compared to the ambient temperature range of 16.1°C to 33.5°C. The relative humidity inside the storage units remained stable between 45 % and 54 %, in contrast to the external humidity range of 45 % to 81.2 %. Additionally, using the Steffe and Singh equation, an increase of 0.512 % in Equilibrium Moisture Content (EMC) of paddy was recorded. Notably, there was no evidence of insect or mold growth after 125 days of storage. This study's remote monitoring system not only marks a significant advancement in hermetic grain storage technology but also contributes to sustainable agricultural practices. It provides a practical, real-time solution to monitor and manage key environmental parameters, ensuring the preservation of grain quality over time.

Design a Deep Learning Model for Dairy Cow Abnormal Behaviour Prediction

When it comes to determining the health and well-being of cattle, it is critical to observe their behaviour. Another strong and cost-effective monitoring approach is a neural network-based monitoring system that analyses time series data from inertial sensors attached to cows. This is one of the most powerful and cost-effective monitoring systems currently available. Even though deep learning has made significant advances in pattern detection, large datasets are still required in many cases. When only a little amount of data is available, data augmentation is a very effective and low-cost pre-processing step for neural network-based systems. To study and analysis, a variety of ways for enhancing inertial sensor data based on cow behavioural data are discussed. Using convolutional neural networks, researchers are attempting to solve the difficult problem of identifying cow behaviour, which is made much more difficult by a scarcity of training data. Our proposed approaches areimproving the effectiveness of deep learning (CNN+LSTM) in the classification of cow behaviour while also lowering the total system costs associated with data collection and labelling.

Cost-Effective Non-Invasive Blood Glucose Monitoring System with Mobile Application for Management of Diabetic Patients

Cost-Effective Non-Invasive Blood Glucose Monitoring System with Mobile Application for Management of Diabetic Patients

Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring.

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.

Prototype Sistem Monitoring Lampu Penerangan Jalan Umum Menggunakan Zigbee dan Esp32

Street lighting, or Public Street Lighting (PJU), is crucial infrastructure in an area, particularly for nighttime traffic illumination. Efficiency and monitoring pose significant challenges in PJU management. Most PJUs lack a monitoring system, making control and malfunction detection difficult. Monitoring is expected to efficiently regulate the on-off schedule and detect malfunctions. This research aims to develop a cost-effective PJU monitoring and efficiency system without replacing many existing devices. The method employs Zigbee technology and Esp32. This system is anticipated to enhance efficiency and detect issues more promptly than before.

IoT-Enabled Real-Time Monitoring System for Electricity Consumption in Maritime SMEs: Design and Evaluation

The growing demand for electrical energy in Small and Medium Enterprises (SMEs) within the maritime sector, necessitates efficient and cost-effective monitoring systems. This paper presents the design and implementation of an Internet of Things (IoT)-based real-time monitoring system for electricity consumption, specifically aimed at maritime SMEs. Using SCT-013-000 and ZMPT101B sensors for current and voltage measurements, the system enables remote monitoring through the Cayenne IoT platform. Experimental results demonstrate an average accuracy rate of 93.94% for current measurement and 98.50% for voltage measurement. Data transmission success rates were 74.00% and 99.02% in two consecutive tests. The proposed system provides an affordable and practical solution for maritime SMEs, contributing to improved energy management, reduced operational costs, and the promotion of sustainable practices. Future work includes the integration of predictive analytics and smart grid technology to further enhance energy efficiency and sustainability in maritime operations.

Implementing a cost-effective soil monitoring system using wireless sensor networks to enhance farming practices for small-scale farmers in developing economy countries

Implementing a cost-effective soil monitoring system using wireless sensor networks to enhance farming practices for small-scale farmers in developing economy countries

Passive-seismic sensor placement optimization for geologic carbon storage

The implementation of passive-seismic monitoring is essential for the geological carbon storage projects. For secure and continuous observing of induced passive-seismic events by CO2 injecting, a ground geophone network would be required. By determining the ideal number of seismic stations within a regular network, recent research has enhanced monitoring capabilities while also accommodating budgetary limitations. The primary objective of the optimal placement strategy for the surface geophone network is to create a cost-effective monitoring system. Given the cost limitations, a restricted quantity of sensors can be deployed at each location to maximize monitoring performance. To address this challenge, our approach involves the P-median stochastic programming formulation. The formulation aims to minimize the expected value of a monitoring target metric, which ultimately results in the optimal placement of detectors exhibiting superior expected behaviors. Our methodology is designed to choose the arrangement of detectors with best performance to improve both the early alarm detection time and localization capabilities of the sensor network. We utilize site-specific passive-seismic scenarios that capture the uncertainty in the characteristics of carbon leakage. The sensor grids provided by the optimization method invariably improve the capacity to detect passive-seismic events in comparison to sensors positioned in a regular grid configuration. We test the effectiveness of our approach on synthetic data based on a carbon storage site named Kimberlina. In conclusion, our approach provides a cost-effective solution for the optimal placement of sensors to achieve superior monitoring performance for the detection of passive-seismic events. The incorporation of site-specific scenarios with stochastic uncertainty coverage allows for more accurate and reliable results, leading to better decision-making for long-term geological carbon storage.

Smart Agriculture in the Digital Age: A Comprehensive IoT-Driven Greenhouse Monitoring System

The integration of Internet of Things (IoT) technologies in agriculture has emerged as a transformative force, revolutionizing traditional farming practices and driving efficiency and sustainability. This paper presents the development and implementation of a cost-effective greenhouse monitoring system utilizing LoRaWAN technology for data communication. The system’s design, deployment, and performance are discussed in detail. Key components include an array of sensors for monitoring environmental parameters and LoRaWAN for long-range, low-power communication. The low-cost nature of the system challenges the notion that advanced agricultural technology is prohibitively expensive, making it accessible to farmers of varying scales. The system’s affordability and realtime data accessibility make it a valuable tool for precision agriculture, contributing to improved crop yields and resource management.

An Automatic-Vertical Profile Monitoring System for Fresh-Saline Water Zones in Coastal Aquifer.

Coastal aquifers are complex systems governed by fresh-saline water interactions and ocean tidal effects. The vertical electrical conductivity (EC) and temperature (T) are general indicators for detecting the fresh-saline water interface (FSI) and sea water intrusion in groundwater wells located in coastal aquifers. In this method brief, we developed a cost-effective Arduino-based automatic-vertical profile monitoring system (A-VPMS) to continuously record vertical EC and T in groundwater wells, with the aim of testing its effectiveness in spatiotemporal monitoring of the FSI in a coastal aquifer located in eastern Korea. By analyzing the high-density EC and T data obtained by the A-VPMS, we evaluated the characteristics of the FSI, such as depth and spatial distribution. Our established EC and T data collection method using the A-VPMS proved to be efficient and reliable, providing an excellent tool for fine-scale temporal and spatial understanding of sea water intrusion. The results of this study demonstrate the potential of the A-VPMS for continuous monitoring of the FSI in coastal aquifers, which is crucial for sustainable management of groundwater resources.

A Hybrid Similarity-Based Method for Wind Monitoring System Deployment Optimization Along Urban Railways

Urban railways in coastal areas are exposed to the risk of extreme weather conditions. A cost-effective and robust wind monitoring system, as a vital part of the railway infrastructure, is essential for ensuring safety and efficiency. However, insufficient sensors along urban rail lines may result in failure to detect local strong winds, thus impacting urban rail safety and operational efficiency. This paper proposes a hybrid method based on historical wind speed data analysis to optimize wind monitoring system deployment. The proposed methodology integrates warning similarity and trend similarity with a linear combination and develops a constrained quadratic programming model to determine the combined weights. The methodology is demonstrated and verified based on a real-world case of an urban rail line. The results show that the proposed method outperforms the single similarity-based method and spatial interpolation approach in terms of both evaluation accuracy and robustness. This study provides a practical data-driven tool for urban rail operators to optimize their wind sensor networks with limited data and resources. It can contribute significantly to enhancing railway system operational efficiency and reducing the hazards on rail infrastructures and facilities under strong wind conditions. Additionally, the novel methodology and evaluation framework can be efficiently applied to the monitoring of other extreme weather conditions, further enhancing urban rail safety.